The reusing of wastewater in agriculture is an extensive practice, whose benefits are multiple given that a large amount of first use water volumes are saved, agricultural production costs are slashed by decreasing the use of artificial fertilizers, lowering contamination to superficial bodies of water (such as rivers, dams and lakes) by avoiding spilling into them large amounts of volumes of treated wastewater without attempting to notably increase crop yield. However, the use of wastewater also represents a health risk, mainly given the content of different pathogenic organisms. Many of these are capable of surviving sufficient amount of time in wastewater, crops and/or soil enabling their transmission to humans either directly or indirectly. Among these organisms are found helminth eggs, parasites commonly known as intestinal worms, whose real risk of infection to product consumers, as well as to agricultural workers and their families, is highly dangerous, given their wide persistence in the environment and their low dosage of infectiousness.
It is important to point out that, traditionally, the biological quality of water has been measured through a bacterial group called fecal coliforms, a group which does not ensure the inactivation or elimination of other types of parasites, which also have their origin in the excretions of infected individuals; so that they are not reliable indicators of the presence of helminth eggs in the contaminated water, excreta or sludge; nor of their behavior during the treatment processes of these. Given this, since 1989 the World Health Organization (WHO) proposed a limit to control the amount of wastewater which is used for spraying, and in 2006 endorsed the importance of controlling helminth eggs in the environment. However, in various world forums, the difficulty of applying these criteria from the World Health Organization has been recognized, given the existence of areas with high helminth egg content, where it is practically impossible to use rentable treatment methods and to the variety of analytical techniques applied to quantify them. To this latter difficulty, we can also add the small numbers of qualified personnel who can identify helminth eggs under the microscope, which constricts the reliability of the results by being subject to a high degree of subjectivity due to the intervention of human interpretation during the analysis, whose central step consists in visually identifying the pathogenic structures.
Generally, the identification of helminth eggs has been resolved in two ways. The first being through specialized personnel, who undertake the identifying and quantifying of helminth eggs in the laboratory. The second form is through image classifier methods and systems, such as the one presently being proposed.
The advancements in algorithms for the processing and recognition of digital images, applied in various scientific fields, present the possibility of employing said tools for the development of a simple system for automatic identification and qualification of different helminth egg genera. The present process and system represents a reliable and objective alternative for the counting of these pathogenic organisms, and the immediate application in multiple environmental studies, at the same time easing the analysis work and taking this benefit to environmental monitoring installations which do not have the benefit of an expert in said identification and qualification.
Within prior art, it can be noted that in the international arena, there exist similar attempts to those in present invention for the detecting and quantifying of helminth eggs. However, the applicability of said programs in environmental samples has not been clearly shown.
For example, Yang et al in Yang Y. S., Park D. K., Kim H. C., Choi M-H and Chai J-Y. (2001) Automatic identification of human helminth eggs on microscopic fecal specimens using digital image processing and an artificial neural network, IEEE Transactions on Biomedical Engineering. 48(6):718-730, found an 84% detection rate in the differentiation of seven different species of eggs, exclusively using feces; so that this segmentation, upon applying three classification features, ends up being inadequate as regards water samples, given that optimal thresholding is not achieved when the eggs to be identified are found surrounded by various objects which are not.
In 2008 Dogantekin et al., en Dogantekin E., Yilmaz M., Dogantekin A., Avci E. and Sengur A. (2008). A robust technique based on invariant moments—ANFIS for recognition of human parasite eggs in microscopic images, Expert Systems with Applications. 35:728-738, they worked on the differentiation of 15 helminth egg species and one protozoan (Giardia Lamblia), for which images were taken from the University of Kansas Parasitological Laboratory internet site, achieving a 93% rate yield. Said images thus obtained, are poor representatives of real water samples, given that the periphery of the eggs presented in said images is free from other types of particles, which implies poor representation of the conditions in environmental samples.
In the same way, Acvi y Varol (2009), in Acvi D. and Varol A. (2009)—An expert diagnosis system for classification of human parasite eggs based on multi-class SVM, Expert Systems with Applications. 36:43-48, by developing a system using photographs from the same website, achieved good results in the classification rate. However, both this type of work as that of Dogantekin et al (2008), were based on the validation of their systems in a parasitological atlas, whose images do not necessarily present the recognition features and difficulties as can be seen in samples of wastewater, sludge, biosolids, soil and/or excreta.
Sauvola and Pietikainen (2000) undertake a local binarization method for the segmentation of objects when a large change in the level of gray in a specific section of the image exists, which allows for separating objects which are deep in the image with improved results.
Additionally, the above cited works use a different classification methodology form the process and system herein proposed, given that, one of the differences found between prior art and present invention, is that prior art bases its identification and quantification of images in a Multi Class Support Vector Machine (MCSV), while present invention proposes and uses three k neighbor classifiers, two of which are based on texture descriptors (LPBs) and one with morphologic and gray level features, using the Mahalonobis metric.
In so far as that which concerns the methodology for undertaking the binarization of the image, prior art seeks to develop the following: a) the gray level threshold, so that in the event that the image is found surrounded by other objects, such as is the case with different qualities of water samples, as well as sludge, biosolids and/or excreta, among others, segmentation errors could be produced, b) the characterization of each egg species, taking reference parameters such as area, perimeter, first Hue invariant moment, entropy, mean gray level, and c) a classifier which is trained in the above mentioned features.
The proposed process and system includes filters and protocols not only to be able to differentiate between species of helminth eggs, but also to be able to differentiate the latter from any other types of objects which are present in the sample, which grants versatility to the identification and quantification in the water, sludge, biosolids and/or excreta samples, among others.
The methodology of image processing proposed in the present invention versus that used in segmentation techniques through which the specific processes for the detection of objects of interest within the image to be processed were developed, with the following basic steps: image acquisition, conversion to gray scale, anisotropic filtering for decreasing noise, binarization using a threshold of Laplacian of Gaussian, binarization of the image using local threshold (Sauvola), binarization intersection, object separation by Watershed, application of morphologic filter, obtaining the features of the object and deciding if it is an egg or not: if it is, identifying the species, verifying the results by means of texture histograms and final label image display.
In the particular case of helminth eggs, the application of the techniques of present invention for developing an automatic process and system for detection and quantification of said eggs in a fast and reliable manner, allows for differentiating from other types of microscopic structures, thereby avoiding over-counting, which is the main problem with traditional identification and quantification techniques, especially in high particle content matrixes such as is the case in wastewater.